'EViews Programming Code for Hong Kong

wfopen  "C:\Users\Maryam\Desktop\BS Studies\PhD Thesis-II\EViews and STATA Progarm Codes\Chapter-7\Hong Kong.wf1"

'****************************************************************************
'Group Plot for pNTpT_HK1, RER_DEF, RER_DEF_NT and aTaNT_HK1
'****************************************************************************
group gA PNT_PT_1 AT_ANT_1
freeze(group_plot) gA.line(x)
group_plot.setelem(1) lcolor(black) symbol(7) lpat(1)
group_plot.setelem(2) lcolor(black) symbol(4) lpat(1)
group_plot.setelem(3) lcolor(black) symbol(1) lpat(1)
group_plot.setelem(3) lcolor(black)
group_plot.options linepat
group_plot.addtext(t) Sectoral Prices and Productivity Gap (Hong Kong & U.S): 1980-2008
group_plot.addtext(b) Year
group_plot.addtext(l) PNT_PT_1
group_plot.addtext(r) AT_ANT_1
'************************************************************
'************************************************************
create y 1980 2008
'importing data from Excel for Hong Kong
import  "C:\Users\Maryam\Desktop\BS Studies\PhD Thesis-II\EViews and STATA Progarm Codes\Chapter-7\Chapter 7.xlsx" range="Hong Kong"
'***************************************************************************************************
'CASE-1: ESTIMATING BALASSA-SAMUELSON EFFECT FOR pNTpT_HK1 & aTaNT_HK1
'***************************************************************************************************
'*************************************************************
'STEP 0: Tests for Unit Root in Individual Time Series
'*************************************************************
'****************************************
'Graph for Hong Kong's pNTpT_HK1
'****************************************
                                        
genr pNTpT_HK1 = pNTpT_HK1
freeze(figure_pNTpT_HK1) pNTpT_HK1.line
figure_pNTpT_HK1.addtext(t) pNTpT_HK1 (Hong Kong):  1980-2008
figure_pNTpT_HK1.addtext(b) Year
figure_pNTpT_HK1.addtext(l) pNTpT_HK1
figure_pNTpT_HK1.legend(off)
                                                 
'We see from the FIGURE that pNTpT_HK1 has time trend to it.  So we would include both an intercept and a time trend in our unit root regression equations. 

'******************************************************
'ADF Unit Root Test for Hong Kong's pNTpT_HK1
'******************************************************
 
freeze(table_7_11_pNTpT_HK1_adf) pNTpT_HK1.uroot(adf,trend,info=sic)

'Note that the SIC automatic lag selection picks lags, p = 2.  The unit root test produces a t-value of -2.92 which is greater than our 5% criterion -3.59.  Thus, at this point, we cannot reject the null of a unit root.

'Now, let's check for white noise.  To do that, I first set all the residuals = 0, then run the ADF test and finally will check for white noise.

genr resid = 0
freeze(mode=overwrite,pNTpT_HK1_adf) pNTpT_HK1.uroot(adf,const,trend,info=sic)
freeze(pNTpT_HK1_adf_correl) resid.correl
 
'Based on the Q-statistic, I conclude that the residuals are white noise.  Putting it all together, I conclude that the pNTpT_HK1 series is not level stationary.

'The next thing I do is test whether the differenced series is stationary using the ADF test.  I once again begin by graphing the (differenced) series.
 
genr pNTpT_HK1diff = d(pNTpT_HK1)
freeze(figure_pNTpT_HK1diff) pNTpT_HK1diff.line
figure_pNTpT_HK1diff.addtext(t) dpNTpT_HK1 (Hong Kong):  1980-2008
figure_pNTpT_HK1diff.addtext(b) Year
figure_pNTpT_HK1diff.addtext(l) DpNTpT_HK1
figure_pNTpT_HK1diff.legend(off)

'From the graph, the series clearly does not have a time trend to it. So, I would test the series for unit with an intercept only.

'So we begin the whole process over again: 

genr pNTpT_HK1diff = d(pNTpT_HK1)
freeze(table_7_11_pNTpT_HK1diff1_adf) pNTpT_HK1diff.uroot(adf,const,info=sic)

'Note that the SIC automatic lag selection picks no lags, p =0.  The unit root test produces a t-value of -2.73 which is still greater than our 5% criterion -2.98.  Thus, we may not reject the null of non-stationarity in first differenced series of pNTpT_HK1.  There is no reason to go further.  The last thing we do is to check ADF regression result for white noise.

genr resid = 0
freeze(mode=overwrite,pNTpT_HK1diff1_adf) pNTpT_HK1diff.uroot(adf,const,info=sic)
freeze(pNTpT_HK1diff1_adf_correl) resid.correl

''Based on the Q-statistic, I conclude that the residuals are white noise.  Putting it all together, I conclude that the pNTpT_HK1 series is greater than I(1).

'**********************************************************
'DF-GLS Unit Root Test for Hong Kong's pNTpT_HK1
'**********************************************************
 
freeze(table_7_11_pNTpT_HK1a_dfgls) pNTpT_HK1.uroot(dfgls,trend,info=sic)

'Note that the SIC automatic lag selection picks lags, p = 1.  The unit root test produces a t-value of -2.85 which is greater than our 5% criterion -3.19.  Thus, at this point, we may not reject the null of unit root.

'Now let's see if the series is difference stationary or not

genr pNTpT_HK1diff = d(pNTpT_HK1)
freeze(table_7_11_pNTpT_HK1bdiff1_dfgls_d) pNTpT_HK1diff.uroot(dfgls,const,info=sic)

'Note that the SIC automatic lag selection picks no lags, p = 0.  The unit root test produces a t-value of -2.14 which is now smaller than our 5% criterion -1.95. Thus, we may reject the null of non-stationarity in first differenced series of pNTpT_HK1.  

''Putting it all together, I conclude that the pNTpT_HK1 series is greater than I(1), a finding compatible with my ADF test results.

'******************************************************
'Graph for Hong Kong's Productivity (aTaNT_HK1)
'******************************************************
                                        
genr aTaNT_HK1 = aTaNT_HK1
freeze(figureaTaNT_HK1) aTaNT_HK1.line
figureaTaNT_HK1.addtext(t) aTaNT_HK1 (Hong Kong):  1980-2008
figureaTaNT_HK1.addtext(b) Year
figureaTaNT_HK1.addtext(l) aTaNT_HK1
figureaTaNT_HK1.legend(off)
                                                 
'We see from the FIGURE that aTaNT_HK1 has time trend to it.  So we would include both an intercept and a time trend in our unit root regression equations. 

'*******************************************************
'ADF Unit Root Test for Hong Kong's Productivity
'*******************************************************
 
freeze(table_7_11_aTaNT_HK1_adf) aTaNT_HK1.uroot(adf,trend,info=sic)

'Note that the SIC automatic lag selection picks lags, p =0.  The unit root test produces a t-value of -0.75 which is greater than our 5% criterion -3.58.  Thus, at this point, we cannot reject the null of a unit root.

'Now, let's check for white noise.  To do that, I first set all the residuals = 0, then run the ADF test and finally will check for white noise.

genr resid = 0
freeze(mode=overwrite,aTaNT_HK1_adf) aTaNT_HK1.uroot(adf,const,trend,info=sic)
freeze(aTaNT_HK1_adf_correl) resid.correl
 
'Based on the Q-statistic, I conclude that the residuals are white noise.  Putting it all together, I conclude that the aTaNT_HK1 series is not level stationary.

'The next thing I do is test whether the differenced series is stationary using the ADF test.  I once again begin by graphing the (differenced) series.
 
genr aTaNT_HK1diff = d(aTaNT_HK1)
freeze(figure_aTaNT_HK1diff) aTaNT_HK1diff.line
figure_aTaNT_HK1diff.addtext(t) daTaNT_HK1 (Hong Kong):  1980-2008
figure_aTaNT_HK1diff.addtext(b) Year
figure_aTaNT_HK1diff.addtext(l) DaTaNT_HK1
figure_aTaNT_HK1diff.legend(off)

'From the graph, the series clearly does not have a time trend to it. So, I would test the series for unit with an intercept only.

'So we begin the whole process over again: 

genr aTaNT_HK1diff = d(aTaNT_HK1)
freeze(table_7_11_aTaNT_HK1diff1_adf) aTaNT_HK1diff.uroot(adf,const,info=sic)

'Note that the SIC automatic lag selection picks no lags, p =0.  The unit root test produces a t-value of -3.73 which is now smaller than our 5% criterion -2.98.  Thus, we may now reject the null of non-stationarity in first differenced series of aTaNT_HK1. There is no reason to go further. The last thing we do is to check ADF regression result for white noise.

genr resid = 0
freeze(mode=overwrite,aTaNT_HK1diff1_adf) aTaNT_HK1diff.uroot(adf,const,info=sic)
freeze(aTaNT_HK1diff1_adf_correl) resid.correl

''Based on the Q-statistic, I conclude that the residuals are white noise.  Putting it all together, I conclude that the aTaNT_HK1 series is I(1).

'************************************************************
'DF-GLS Unit Root Test for Hong Kong's Productivity
'************************************************************
 
freeze(table_7_11_aTaNT_HK1_dfgls) aTaNT_HK1.uroot(dfgls,trend,info=sic)

'Note that the SIC automatic lag selection picks lags, p = 1.  The unit root test produces a t-value of -1.58 which is greater than our 5% criterion -3.19.  Thus, at this point, we may not reject the null of a unit root.
 
'Now let's see if the series is difference stationary or not

genr aTaNT_HK1diff = d(aTaNT_HK1)
freeze(table_7_11_aTaNT_HK11diff1_dfgls) aTaNT_HK1diff.uroot(dfgls,const,info=sic)

'Note that the SIC automatic lag selection picks no lags, p = 1.  The unit root test produces a t-value of -3.73 which is smaller than our 5% criterion -1.95. Thus, we may reject the null of non-stationarity in first differenced series of aTaNT_HK1.  

''Putting it all together, I conclude that the aTaNT_HK1 series is I(1), a finding compatible with my ADF test results.

'*********************************************
'Single Equation Cointegration Methods
'*********************************************

'**********************************************************
''Graph the suspected cointegrated series together
'**********************************************************

'The first step is to plot a graph of the suspected series.  This is very important!

group g1 pNTpT_HK1 aTaNT_HK1
freeze(figure7_11a) g1.line(x)
figure7_11a.setelem(1) lcolor(black) 
figure7_11a.setelem(2) lcolor(black) lpat(8)
figure7_11a.options linepat
figure7_11a.addtext(t) pNTpT_HK1 and aTaNT_HK1 (Hong Kong & U.S): 1980-2008
figure7_11a.addtext(b) Year
figure7_11a.addtext(l) pNTpT_HK1
figure7_11a.addtext(r) aTaNT_HK1

''*******************************************************
''S1.A.Engle-Granger Approach to Cointegration
'*******************************************************
 
freeze(table_7_11_egc) g1.coint(method=eg)

'The null hypothesis will be rejected as suggested by sample statistics.

''******************************************
''S1.B.Error Correction Model (ECM)
'*******************************************
''**********************************************
'Selecting the number of lags in the VAR
'***********************************************

'NOTE: We do this because we need to have the "right" number of lags when it comes time to estimate our VEC model and test for cointegration.

var table_7_11_var1.ls 1 4   g1
freeze(table_7_11_var1_lagtest1) table_7_11_var1.laglen(4)
freeze(table_7_11_var1_lagtest2) table_7_11_var1.testlags

'The laglength test above indicates that the VAR has 2 lags.
 
var table_7_11_var2.ls 1 2  g1
freeze(table_7_11_var2_artest1) table_7_11_var2.correl
freeze(table_7_11_var2_artest2) table_7_11_var2.qstats(12)
freeze(table_7_11_var2_artest3) table_7_11_var2.arlm(12)

'The residuals are absolutely white noise.

'We now try different lags of d(aTaNT_HK1), comparing SIC values across specifications.

genr resid = 0
equation eg.ls pNTpT_HK1 c aTaNT_HK1
genr ec = resid

var table_7_11_eg2a.ls 0 0 d(pNTpT_HK1)   @  c ec(-1) d(pNTpT_HK1(-1)) d(pNTpT_HK1(-2)) 

var table_7_11_eg2b.ls 0 0 d(pNTpT_HK1)   @  c ec(-1) d(pNTpT_HK1(-1)) d(pNTpT_HK1(-2)) d(aTaNT_HK1(-1))

var table_7_11_eg2c.ls 0 0 d(pNTpT_HK1)   @  c ec(-1) d(pNTpT_HK1(-1)) d(pNTpT_HK1(-2)) d(aTaNT_HK1(-1)) d(aTaNT_HK1(-2))

'The evidence suggests that Model C is best.  Now we test that model for serial correlation.

var table_7_11_eg2c.ls 0 0 d(pNTpT_HK1)   @   c ec(-1) d(pNTpT_HK1(-1)) d(pNTpT_HK1(-2)) d(aTaNT_HK1(-1)) d(aTaNT_HK1(-2))
freeze(table_7_11_eg2c1_artest1) table_7_11_eg2c.correl
freeze(table_7_11_eg2c2_artest2) table_7_11_eg2c.qstats(12)
freeze(table_7_11_eg2c3_artest3) table_7_11_eg2c.arlm(12)

'The residuals are absolutely white noise.

''*************************
''Estimating EC Model  
'**************************

'We'll now take the above specified model and turn it into an ECM. We shall run NW-HAC least squares model for establishing error correction mechanism.

'We now estimate the corresponding ECM:

equation table_7_11_ecm.ls(n) d(pNTpT_HK1) c ec(-1) d(pNTpT_HK1(-1)) d(pNTpT_HK1(-2)) d(aTaNT_HK1(-1)) d(aTaNT_HK1(-2))

'Note that the SR effect is insignificant as the error correction coefficient -0.15 is statistically insignificant even at 10% significance level.

''******************************************
'Multivariate Cointegration Approach
'******************************************

''********************************************************
''Check if the VAR (2) model is dynamically stable
'*********************************************************
freeze(table_7_11_var2_varstable) table_7_11_var2.arroots(graph)

'The model is dynamically stable.

''**********************************************************************
''M1.A & M1.B: Identifying the number of cointegrating vectors
'***********************************************************************

'Having identified the appropriate number of lags to put in, I now go on to test for the appropriate number of cointegrating equations.

freeze(table_7_11_var2_coint) table_7_11_var2.coint(s,2)

'This command estimates all possible combinations of constants and trends in the level data series and the cointegrating equations. All the results indicate 0 cointegrating vectors.
'
'GENERAL NOTE:, in practice, cases 1 and 5 are rarely used. One should use case 1 only if one knows that all series have zero mean. Case 5 may provide a good fit in-sample but will produce implausible forecasts out-of-sample. As a rough guide, use case 2 if none of the series appear to have a trend. For trending series, use case 3 if you believe all trends are stochastic; if you believe some of the series are trend stationary, use case 4.

'Note that the 5 cases are identified under "Johansen cointegration test" in EViews. They run from most restrictive (no constants in either the level series or CEs) to most general (trend terms in both the level series and CEs).

'CONCLUSION:  I conclude that pNTpT_HK1 and aTaNT_HK1 are not cointegrated in the Hong Kong's data.


